查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting from Koper, Slovenia, by NewsRx journalists, research stated, “In the task of modeling user preferences for movie recommender systems, recent research has demonstrated the benefits of describing movies with their eudaimonic and hedonic scores (E and H scores), which reflect the depth of their message and the level of fun experience they provide, respectively. So far, the labeling of movies with their E and H scores has been done manually using a dedicated instrument (a questionnaire), which is time-consuming.” Financial support for this research came from CogniCom grant - University of Primorska. The news correspondents obtained a quote from the research from the University of Primorska, “To address this issue, we propose an automatic approach for predicting E and H scores. Specifically, we collected E and H scores of 709 movies from 370 users (with a total of 3699 records), augmented this dataset with metadata, audio, and low-level and high-level visual features, and trained machine learning models for predicting the E and H scores of movies. This study investigates the use of machine learning models in predicting the E and H scores of movies using various feature sets, including audio, low-level and high-level visual features, and metadata. We compared the performance of predictive models using different combinations of features with the majority classifier as the baseline approach. The results demonstrate that our proposed machine learning based models significantly outperform the baseline in predicting E and H scores, particularly when leveraging metadata features. Specifically, the random forest classifier achieved a 20% increase in ROC AUC compared to the baseline when predicting both the E score and the H score. These improvements were found to be statistically significant.”
查看更多>>摘要：Researchers detail new data in Robotics. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, "A collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task." Financial support for this research came from Key R&D Program of Jiangsu Province. The news correspondents obtained a quote from the research from Donghua University, "Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage."
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Bratislava, Slovakia, by NewsRx editors, the research stated, "In this paper, I explore Derrida's concept of exteriorization in relation to texts generated by machine learning. I first discuss Heidegger's view of machine creation and then present Derrida's criticism of Heidegger." Financial supporters for this research include John Templeton Foundation, Slovak Academy of Sciences. The news reporters obtained a quote from the research from the Slovak Academy of Sciences, "I explain the concept of iterability, which is the central notion on which Derrida's criticism is based. The thesis defended in the paper is that Derrida's account of iterability provides a helpful framework for understanding the phenomenon of machine learning-generated literature. His account of textuality highlights the incalculability and mechanical elements characteristic of all texts, including machine-generated texts. By applying Derrida's concept to the phenomenon of machine creation, we can deconstruct the distinction between human and non-human creation." According to the news reporters, the research concluded: "As I propose in the conclusion to this paper, this provides a basis on which to consider potential positive uses of machine learning." This research has been peer-reviewed.
查看更多>>摘要：Researchers detail new data in Artificial Intelligence. According to news reporting originating from Wuhu, People's Republic of China, by NewsRx correspondents, research stated, "Artificial intelligence (AI) is revolutionizing consumer-provider interactions by changing the nature of online purchases. This study uses the social support theory to investigate consumer purchase intentions by combining AI technology, consumer social media engagement, and consumer experience." Funders for this research include National Natural Science Foundation of China (NSFC), Program of Philosophy and Social Science Foundation of Anhui Province, China, Program of Social Science Innovation Development of Anhui Province, China, National Social Science Foundation, The 2022 Anhui Province University Discipline (Major) Top Talent Academic Support Project, China Postdoctoral Science Foundation. Our news editors obtained a quote from the research from Anhui Polytechnic University, "Online surveys are conducted with 467 Chinese social media users who had experience with online purchasing and AI technology. Partial Least Squares Structural Equation Modelling (PLS-SEM) is used to examine the data and proposed hypothesis. This study finds that AI positively affects consumer experience and consumer engagement on social media. Similarly, a positive relationship exists between social media engagement and consumer experience, leading to a more satisfied consumer and amplified purchase intentions. Additionally, affective attachment moderates the relationship between consumer satisfaction and purchase intention. The results reveal that AI can be used on social media to improve consumer experience and increase customer satisfaction levels and purchase intention. We also provide tips for developing flawless service business models."
查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news reporting from Chengdu, People's Republic of China, by NewsRx journalists, research stated, "High entropy alloys (HEAs) have excellent properties because they can form simple solid solution (SS) phases, including body-centered cubic (BCC) phase, face-centered cubic (FCC) phase, or FCC + BCC phase, so phase prediction is the first step in alloy design." Financial supporters for this research include Joint Fund of The National Natural Science Foundation of China And The Karst Science Research Center of Guizhou Province. The news editors obtained a quote from the research from Chengdu University: "In current research, machine learning (ML) approach had been widely used to guide the discovery and design of materials. The prediction of HEAs phase structure based on machine learning (ML) is a hot topic. In this work, five ML algorithms were utilized to predict HEAs for SS and amorphous (AM) phases based on 399 collected data sets, including 120 BCC alloys, 87 FCC alloys, 82 BCC + FCC alloys and 110 a.m. alloys. To enhance the model's accuracy, grid search and K-fold cross validation were used to optimize performance. Valence electron concentration (VEC) and DHmix exhibit high importance in prediction in compared to other parameters. The results show that the random forest can effectively distinguish BCC phase, FCC phase, mixed solid solution phase (FCC + BCC) and AM, with an accuracy is 0.87."
查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating in Guangzhou, People's Republic of China, by NewsRx journalists, research stated, "For the purpose of robot automatic welding based on a laser stripe welding seam tracking system, a weld noise image restoration algorithm based on vector quantization and synthetic fractal noise is proposed to solve the problems caused by strong noise interference and limited noise dataset in welding seam tracking. The weld noise image restoration is a process of noise loss." Financial support for this research came from National Natural Science Foundation of Guangdong Province. The news reporters obtained a quote from the research from the South China University of Technology, "To achieve this process, a weld noise image restoration learning model based on feature encoding, vector quantization, and feature decoding is constructed, which includes feature encoding to extract image features, vector quantization to produce noise loss, and feature decoding to complete image restoration. To enhance the generalization ability of the weld noise image restoration model and overcome the difficulty of limited data collection in on-site welding, a synthetic welding noise model is proposed based on fractal theory, multidimensional Gaussian distribution, and random region generation algorithm. A large-scale training dataset is generated by randomly initializing parameters, and the potential mapping rela-tionship between the weld noise image and the noise-free image is constructed. Compared with the limited dataset obtained in on-site welding, the synthetic dataset makes the image restoration model more generalizable, and the similarity between the restored welding seam image and the original image reaches 0.85."
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Modena, Italy, by NewsRx correspondents, research stated, "The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine." Our news editors obtained a quote from the research from the University of Modena and Reggio Emilia, "A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer. Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly twothirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival. The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides." According to the news editors, the research concluded: "Further technologic improvement would help widening AI's adoption in prostate pathology, as well as expanding its prognostic predictive potential." This research has been peer-reviewed.
查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting from Hamburg, Germany, by NewsRx journalists, research stated, "Accurate short-term predictions of phase-resolved water wave conditions are crucial for decision-making in ocean engineering. However, the initialization of remote-sensing-based wave prediction models first requires a reconstruction of wave surfaces from sparse measurements like radar." Financial support for this research came from German Research Foundation (DFG). The news correspondents obtained a quote from the research from the Hamburg University of Technology, "Existing reconstruction methods either rely on computationally intensive optimization procedures or simplistic modelling assumptions that compromise the real-time capability or accuracy of the subsequent prediction process. We therefore address these issues by proposing a novel approach for phase-resolved wave surface reconstruction using neural networks based on the U-Net and Fourier neural operator (FNO) architectures. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids, that is generated by the high-order spectral method for wave simulation and a geometric radar modelling approach. The investigation reveals that both models deliver accurate wave reconstruction results and show good generalization for different sea states when trained with spatio-temporal radar data containing multiple historic radar snapshots in each input."
查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating from Alexandria, Egypt, by NewsRx correspondents, research stated, "With the digitization of the entire world and huge requirements of understanding unknown patterns from the data, clustering becomes an important research area. The quick and accurate division of large datasets with a range of properties or features becomes challenging." Our news editors obtained a quote from the research from the Egypt-Japan University of Science and Technology, "The parallel implementation of clustering algorithms must satisfy stringent computational requirements to handle large amounts of data. This can be achieved by designing a GPU based optimal computational model with a heuristic approach. Swarm Intelligence (SI), a family of bioinspired algorithms, that has been effectively applied to a number of real-world clustering problems. The Gravitational Search Algorithm (GSA) is a heuristic search optimization approach based on Newton's Law of Gravitation and mass interactions. Although it has a slow searching rate in the last iterations, this strategy has been proved to be capable of discovering the global optimum. This paper presents GPU based hybrid parallel algorithms for data clustering. A newly developed, hybrid Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) i.e., PSOGSA achieves the global optima. PSOGSA utilizes novel training methods for enhanced Neural Networks (NN) in order to examine the efficiency of algorithms and resolves the challenges of trapping in local minima. This also shows the sluggish convergence rate of standard evolutionary learning algorithms. The Nearest Neighbour Partition (Partitioning of the Neighbourhood) algorithm can be used to improve the performance of NN. A parallel version of Hybrid PSOGSA with NN is implemented to achieve optimal results with better computational time." According to the news editors, the research concluded: "Compared to the CPU-based regular PSO, the suggested Hybrid PSOGSA with NN achieved optimal clustering with 71% improved computational time." This research has been peer-reviewed.
查看更多>>摘要：Investigators publish new report on Machine Learning. According to news originating from Hanoi, Vietnam, by NewsRx correspondents, research stated, "Forest biomass provides a quantitative assessment for carbon stock marketing on a national or regional scale. Some countries have committed to net zero carbon emissions, so proper biomass estimations are essential." Financial support for this research came from Ministry of Science and Technology of Vietnam (MOST). Our news journalists obtained a quote from the research from Vietnam National University, "This study investigates the uses of machine learning (LightGBM, XGBoost), in which hyperparameters were tuned by Bayesian-based Optimisers and a novel Tasmanian Devil Optimisation algorithm for estimates of aboveground biomass (AGB) using Sentinel 1A, Landsat images, and ground survey data. A province in the northern part of Vietnam was selected as a case study since the change in land cover has been considered crucial. The models were optimized/trained and validated using statistical indicators, namely, root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The trained models were further explained using SHAP values to understand better how they perform and the contribution of each feature to the overall estimates. The results showed that the three indicators of the proposed model were statistically better than those of the reference methods. Specifically, the hybrid model ended up at RMSE -13.87, MAE - 10.62, and R2 - 0.79 for the estimation of AGB. Based on the experience, such hybrid integration can be recommended as an alternative solution for biomass estimation." According to the news editors, the research concluded: "In a broader context, the fast growth of machine learning and optimization algorithms has created new scientifically sound solutions for a better analysis of forest cover." This research has been peer-reviewed.
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